考虑高推力与低推力推进权衡的在轨服务优化框架
On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff
AI总结：
本文提出一种在轨服务物流优化框架，整合用户定义轨迹模型并权衡推进技术与轨迹，采用混合整数线性规划和滚动时域方法优化服务航天器运行，可支持基础设施的短期调度与长期规划。
摘要：
本文提出一种在轨服务物流优化框架，能够对可持续服务基础设施进行短期运行调度和长期战略规划，该基础设施包含由轨道仓库支持的高推力、低推力和/或多模式服务航天器。所提框架通过整合用户定义的轨迹模型，并在考虑推进技术和轨迹权衡的情况下优化物流运行，对最先进的在轨服务物流优化方法进行了泛化。利用混合整数线性规划（Mixed-Integer Linear Programming）在给定时间段内寻找服务航天器的最优运行方案，同时采用滚动时域（Rolling Horizon）方法考虑长期时间范围以应对服务需求的不确定性。通过多项分析，验证了该框架在自动权衡高推力与低推力推进系统，以支持在轨服务基础设施短期运行调度和长期战略规划方面的价值。
英文摘要：
This paper proposes an on-orbit servicing logistics optimization framework that is capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-Integer Linear Programming is leveraged to find the optimal operations of the servicers over a given period, while the Rolling Horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
